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231224s2016 xx |||||o 00| ||eng c |
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|a 10.1109/TPAMI.2015.2481404
|2 doi
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|a pubmed24n0844.xml
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|a (DE-627)NLM253184150
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|a (NLM)26415154
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|a DE-627
|b ger
|c DE-627
|e rakwb
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| 041 |
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|a eng
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| 100 |
1 |
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|a Zhao, Ruiqi
|e verfasserin
|4 aut
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| 245 |
1 |
0 |
|a Labeled Graph Kernel for Behavior Analysis
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| 264 |
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1 |
|c 2016
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| 336 |
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
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|2 rdamedia
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| 338 |
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|a ƒa Online-Ressource
|b cr
|2 rdacarrier
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| 500 |
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|a Date Completed 09.05.2018
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| 500 |
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|a Date Revised 11.01.2019
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| 500 |
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|a published: Print-Electronic
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| 500 |
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|a Citation Status MEDLINE
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| 520 |
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|a Automatic behavior analysis from video is a major topic in many areas of research, including computer vision, multimedia, robotics, biology, cognitive science, social psychology, psychiatry, and linguistics. Two major problems are of interest when analyzing behavior. First, we wish to automatically categorize observed behaviors into a discrete set of classes (i.e., classification). For example, to determine word production from video sequences in sign language. Second, we wish to understand the relevance of each behavioral feature in achieving this classification (i.e., decoding). For instance, to know which behavior variables are used to discriminate between the words apple and onion in American Sign Language (ASL). The present paper proposes to model behavior using a labeled graph, where the nodes define behavioral features and the edges are labels specifying their order (e.g., before, overlaps, start). In this approach, classification reduces to a simple labeled graph matching. Unfortunately, the complexity of labeled graph matching grows exponentially with the number of categories we wish to represent. Here, we derive a graph kernel to quickly and accurately compute this graph similarity. This approach is very general and can be plugged into any kernel-based classifier. Specifically, we derive a Labeled Graph Support Vector Machine (LGSVM) and a Labeled Graph Logistic Regressor (LGLR) that can be readily employed to discriminate between many actions (e.g., sign language concepts). The derived approach can be readily used for decoding too, yielding invaluable information for the understanding of a problem (e.g., to know how to teach a sign language). The derived algorithms allow us to achieve higher accuracy results than those of state-of-the-art algorithms in a fraction of the time. We show experimental results on a variety of problems and datasets, including multimodal data
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| 650 |
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4 |
|a Journal Article
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| 650 |
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4 |
|a Research Support, N.I.H., Extramural
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| 700 |
1 |
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|a Martinez, Aleix M
|e verfasserin
|4 aut
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| 773 |
0 |
8 |
|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g 38(2016), 8 vom: 10. Aug., Seite 1640-50
|w (DE-627)NLM098212257
|x 1939-3539
|7 nnns
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| 773 |
1 |
8 |
|g volume:38
|g year:2016
|g number:8
|g day:10
|g month:08
|g pages:1640-50
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| 856 |
4 |
0 |
|u http://dx.doi.org/10.1109/TPAMI.2015.2481404
|3 Volltext
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|a AR
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|d 38
|j 2016
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|h 1640-50
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